Recent advances in generative modeling have been integral to the success of many important data-driven tasks such as image generation [1]}, [2]}, drug discovery [3]} and video prediction [4]}. Generative models can be defined as taking in data of some type, learning the defining features of the data, and outputting new data of the same type and with the same features; in other words, learning a probability distribution from taking in a limited subset of samples from the underlying distribution. Learning complex probability distributions is a challenging problem for a classical computer, which is why improvements in quantum computing technology have prompted investigations into quantum generative modeling [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}. This is partly because of how intuitively it can be embedded into a quantum computing framework, as probability distributions can be generated simply through measurements of quantum states. As hardware further improves, generative modeling has been identified as one of the leading candidates to achieve quantum advantage [13]}, [14]}, [11]}, [7]}, [17]}.
